MapReduce: Simplified Data Processing on Large Clusters pptx

We realized that most of our computations involved applying a map op-eration to each logical “record” in our input in order to compute a set of intermediate key/value pairs, and then ap

MapReduce: Simplified Data Processing on Large Clusters

Jeffrey Dean and Sanjay Ghemawat jeff@google.com, sanjay@google.com

Google, Inc.

Abstract

MapReduce is a programming model and an

associ-ated implementation for processing and generating large

data sets Users specify a map function that processes a

key/value pair to generate a set of intermediate key/value

pairs, and a reduce function that merges all intermediate

values associated with the same intermediate key Many

real world tasks are expressible in this model, as shown

in the paper

Programs written in this functional style are

automati-cally parallelized and executed on a large cluster of

com-modity machines The run-time system takes care of the

details of partitioning the input data, scheduling the

pro-gram’s execution across a set of machines, handling

ma-chine failures, and managing the required inter-mama-chine

communication This allows programmers without any

experience with parallel and distributed systems to

eas-ily utilize the resources of a large distributed system

Our implementation of MapReduce runs on a large

cluster of commodity machines and is highly scalable:

a typical MapReduce computation processes many

ter-abytes of data on thousands of machines Programmers

find the system easy to use: hundreds of MapReduce

pro-grams have been implemented and upwards of one

thou-sand MapReduce jobs are executed on Google’s clusters

every day

1 Introduction

Over the past five years, the authors and many others at

Google have implemented hundreds of special-purpose

computations that process large amounts of raw data,

such as crawled documents, web request logs, etc., to

compute various kinds of derived data, such as inverted

indices, various representations of the graph structure

of web documents, summaries of the number of pages

crawled per host, the set of most frequent queries in a

given day, etc Most such computations are conceptu-ally straightforward However, the input data is usuconceptu-ally large and the computations have to be distributed across hundreds or thousands of machines in order to finish in

a reasonable amount of time The issues of how to par-allelize the computation, distribute the data, and handle failures conspire to obscure the original simple compu-tation with large amounts of complex code to deal with these issues

As a reaction to this complexity, we designed a new abstraction that allows us to express the simple computa-tions we were trying to perform but hides the messy de-tails of parallelization, fault-tolerance, data distribution and load balancing in a library Our abstraction is

in-spired by the map and reduce primitives present in Lisp

and many other functional languages We realized that

most of our computations involved applying a map

op-eration to each logical “record” in our input in order to compute a set of intermediate key/value pairs, and then

applying a reduce operation to all the values that shared

the same key, in order to combine the derived data ap-propriately Our use of a functional model with user-specified map and reduce operations allows us to paral-lelize large computations easily and to use re-execution

as the primary mechanism for fault tolerance

The major contributions of this work are a simple and powerful interface that enables automatic parallelization and distribution of large-scale computations, combined with an implementation of this interface that achieves high performance on large clusters of commodity PCs Section 2 describes the basic programming model and gives several examples Section 3 describes an imple-mentation of the MapReduce interface tailored towards our cluster-based computing environment Section 4 de-scribes several refinements of the programming model that we have found useful Section 5 has performance measurements of our implementation for a variety of tasks Section 6 explores the use of MapReduce within Google including our experiences in using it as the basis

for a rewrite of our production indexing system

Sec-tion 7 discusses related and future work

The computation takes a set of input key/value pairs, and

produces a set of output key/value pairs. The user of

the MapReduce library expresses the computation as two

functions: Map and Reduce.

Map, written by the user, takes an input pair and

pro-duces a set of intermediate key/value pairs The

MapRe-duce library groups together all intermediate values

asso-ciated with the same intermediate keyI and passes them

to the Reduce function.

The Reduce function, also written by the user, accepts

an intermediate keyI and a set of values for that key It

merges together these values to form a possibly smaller

set of values Typically just zero or one output value is

produced per Reduce invocation The intermediate

val-ues are supplied to the user’s reduce function via an

iter-ator This allows us to handle lists of values that are too

large to fit in memory

Consider the problem of counting the number of

oc-currences of each word in a large collection of

docu-ments The user would write code similar to the

follow-ing pseudo-code:

map(String key, String value):

// key: document name

// value: document contents

for each word w in value:

EmitIntermediate(w, "1");

reduce(String key, Iterator values):

// key: a word

// values: a list of counts

int result = 0;

for each v in values:

result += ParseInt(v);

Emit(AsString(result));

The map function emits each word plus an associated

count of occurrences (just ‘1’ in this simple example)

The reduce function sums together all counts emitted

for a particular word

In addition, the user writes code to fill in a mapreduce

specification object with the names of the input and

out-put files, and optional tuning parameters The user then

invokes the MapReduce function, passing it the

specifi-cation object The user’s code is linked together with the

MapReduce library (implemented in C++) Appendix A

contains the full program text for this example

Even though the previous pseudo-code is written in terms

of string inputs and outputs, conceptually the map and reduce functions supplied by the user have associated types:

map (k1,v1) → list(k2,v2) reduce (k2,list(v2)) → list(v2) I.e., the input keys and values are drawn from a different domain than the output keys and values Furthermore, the intermediate keys and values are from the same do-main as the output keys and values

Our C++ implementation passes strings to and from the user-defined functions and leaves it to the user code

to convert between strings and appropriate types

Here are a few simple examples of interesting programs that can be easily expressed as MapReduce computa-tions

Distributed Grep: The map function emits a line if it matches a supplied pattern The reduce function is an identity function that just copies the supplied intermedi-ate data to the output

func-tion processes logs of web page requests and outputs hURL, 1i The reduce function adds together all values for the same URL and emits a hURL, total counti pair

htarget, sourcei pairs for each link to a target URL found in a page named source The reduce function concatenates the list of all source URLs as-sociated with a given target URL and emits the pair: htarget, list(source)i

Term-Vector per Host: A term vector summarizes the most important words that occur in a document or a set

of documents as a list ofhword, f requencyi pairs The map function emits a hhostname, term vectori pair for each input document (where the hostname is extracted from the URL of the document) The re-duce function is passed all per-document term vectors for a given host It adds these term vectors together, throwing away infrequent terms, and then emits a final hhostname, term vectori pair

Master

(1) fork

worker

(1) fork

worker

(1) fork

(2) assign map

(2) assign reduce

split 0

split 1

split 2

split 3

split 4

output file 0

(6) write

worker

(3) read

worker

(4) local write

Map phase

Intermediate files (on local disks)

file 1

Input

files

(5) remote read

Reduce phase

Output files

Figure 1: Execution overview

Inverted Index: The map function parses each

docu-ment, and emits a sequence ofhword, document IDi

pairs The reduce function accepts all pairs for a given

word, sorts the corresponding document IDs and emits a

hword, list(document ID)i pair The set of all output

pairs forms a simple inverted index It is easy to augment

this computation to keep track of word positions

Distributed Sort: The map function extracts the key

from each record, and emits ahkey, recordi pair The

reduce function emits all pairs unchanged This

compu-tation depends on the partitioning facilities described in

Section 4.1 and the ordering properties described in

Sec-tion 4.2

Many different implementations of the MapReduce

in-terface are possible The right choice depends on the

environment For example, one implementation may be

suitable for a small shared-memory machine, another for

a large NUMA multi-processor, and yet another for an

even larger collection of networked machines

This section describes an implementation targeted

to the computing environment in wide use at Google:

large clusters of commodity PCs connected together with switched Ethernet [4] In our environment:

(1) Machines are typically dual-processor x86 processors running Linux, with 2-4 GB of memory per machine

(2) Commodity networking hardware is used – typically either 100 megabits/second or 1 gigabit/second at the machine level, but averaging considerably less in over-all bisection bandwidth

(3) A cluster consists of hundreds or thousands of ma-chines, and therefore machine failures are common

(4) Storage is provided by inexpensive IDE disks at-tached directly to individual machines A distributed file system [8] developed in-house is used to manage the data stored on these disks The file system uses replication to provide availability and reliability on top of unreliable hardware

(5) Users submit jobs to a scheduling system Each job consists of a set of tasks, and is mapped by the scheduler

to a set of available machines within a cluster

3.1 Execution Overview

The Map invocations are distributed across multiple

machines by automatically partitioning the input data

into a set of M splits. The input splits can be

pro-cessed in parallel by different machines Reduce

invoca-tions are distributed by partitioning the intermediate key

space intoR pieces using a partitioning function (e.g.,

hash(key) mod R) The number of partitions (R) and

the partitioning function are specified by the user

Figure 1 shows the overall flow of a MapReduce

op-eration in our implementation When the user program

calls the MapReduce function, the following sequence

of actions occurs (the numbered labels in Figure 1

corre-spond to the numbers in the list below):

1 The MapReduce library in the user program first

splits the input files intoM pieces of typically 16

megabytes to 64 megabytes (MB) per piece

(con-trollable by the user via an optional parameter) It

then starts up many copies of the program on a

clus-ter of machines

2 One of the copies of the program is special – the

master The rest are workers that are assigned work

by the master There areM map tasks and R reduce

tasks to assign The master picks idle workers and

assigns each one a map task or a reduce task

3 A worker who is assigned a map task reads the

contents of the corresponding input split It parses

key/value pairs out of the input data and passes each

pair to the user-defined Map function The

interme-diate key/value pairs produced by the Map function

are buffered in memory

4 Periodically, the buffered pairs are written to local

disk, partitioned intoR regions by the partitioning

function The locations of these buffered pairs on

the local disk are passed back to the master, who

is responsible for forwarding these locations to the

reduce workers

5 When a reduce worker is notified by the master

about these locations, it uses remote procedure calls

to read the buffered data from the local disks of the

map workers When a reduce worker has read all

in-termediate data, it sorts it by the inin-termediate keys

so that all occurrences of the same key are grouped

together The sorting is needed because typically

many different keys map to the same reduce task If

the amount of intermediate data is too large to fit in

memory, an external sort is used

6 The reduce worker iterates over the sorted

interme-diate data and for each unique intermeinterme-diate key

en-countered, it passes the key and the corresponding

set of intermediate values to the user’s Reduce

func-tion The output of the Reduce function is appended

to a final output file for this reduce partition

7 When all map tasks and reduce tasks have been completed, the master wakes up the user program

At this point, the MapReduce call in the user pro-gram returns back to the user code

After successful completion, the output of the mapre-duce execution is available in theR output files (one per reduce task, with file names as specified by the user) Typically, users do not need to combine theseR output files into one file – they often pass these files as input to another MapReduce call, or use them from another dis-tributed application that is able to deal with input that is partitioned into multiple files

3.2 Master Data Structures

The master keeps several data structures For each map

task and reduce task, it stores the state (idle, in-progress,

or completed), and the identity of the worker machine

(for non-idle tasks)

The master is the conduit through which the location

of intermediate file regions is propagated from map tasks

to reduce tasks Therefore, for each completed map task, the master stores the locations and sizes of theR inter-mediate file regions produced by the map task Updates

to this location and size information are received as map tasks are completed The information is pushed

incre-mentally to workers that have in-progress reduce tasks.

3.3 Fault Tolerance

Since the MapReduce library is designed to help process very large amounts of data using hundreds or thousands

of machines, the library must tolerate machine failures gracefully

Worker Failure

The master pings every worker periodically If no re-sponse is received from a worker in a certain amount of time, the master marks the worker as failed Any map tasks completed by the worker are reset back to their

ini-tial idle state, and therefore become eligible for

schedul-ing on other workers Similarly, any map task or reduce

task in progress on a failed worker is also reset to idle

and becomes eligible for rescheduling

Completed map tasks are re-executed on a failure be-cause their output is stored on the local disk(s) of the failed machine and is therefore inaccessible Completed reduce tasks do not need to be re-executed since their output is stored in a global file system

When a map task is executed first by workerA and then later executed by workerB (because A failed), all

execution Any reduce task that has not already read the

data from workerA will read the data from worker B

MapReduce is resilient to large-scale worker failures

For example, during one MapReduce operation, network

maintenance on a running cluster was causing groups of

80 machines at a time to become unreachable for

sev-eral minutes The MapReduce master simply re-executed

the work done by the unreachable worker machines, and

continued to make forward progress, eventually

complet-ing the MapReduce operation

Master Failure

It is easy to make the master write periodic checkpoints

of the master data structures described above If the

mas-ter task dies, a new copy can be started from the last

checkpointed state However, given that there is only a

single master, its failure is unlikely; therefore our

cur-rent implementation aborts the MapReduce computation

if the master fails Clients can check for this condition

and retry the MapReduce operation if they desire

Semantics in the Presence of Failures

When the user-supplied map and reduce operators are

de-terministic functions of their input values, our distributed

implementation produces the same output as would have

been produced by a non-faulting sequential execution of

the entire program

We rely on atomic commits of map and reduce task

outputs to achieve this property Each in-progress task

writes its output to private temporary files A reduce task

produces one such file, and a map task producesR such

files (one per reduce task) When a map task completes,

the worker sends a message to the master and includes

the names of theR temporary files in the message If

the master receives a completion message for an already

completed map task, it ignores the message Otherwise,

it records the names ofR files in a master data structure

When a reduce task completes, the reduce worker

atomically renames its temporary output file to the final

output file If the same reduce task is executed on

multi-ple machines, multimulti-ple rename calls will be executed for

the same final output file We rely on the atomic rename

operation provided by the underlying file system to

guar-antee that the final file system state contains just the data

produced by one execution of the reduce task

The vast majority of our map and reduce operators are

deterministic, and the fact that our semantics are

equiv-alent to a sequential execution in this case makes it very

havior When the map and/or reduce operators are

non-deterministic, we provide weaker but still reasonable se-mantics In the presence of non-deterministic operators, the output of a particular reduce taskR1is equivalent to the output forR1produced by a sequential execution of the non-deterministic program However, the output for

a different reduce taskR2may correspond to the output for R2 produced by a different sequential execution of the non-deterministic program

Consider map taskM and reduce tasks R1 andR2 Lete(Ri) be the execution of Ri that committed (there

is exactly one such execution) The weaker semantics arise becausee(R1) may have read the output produced

by one execution of M and e(R2) may have read the output produced by a different execution ofM

3.4 Locality

Network bandwidth is a relatively scarce resource in our computing environment We conserve network band-width by taking advantage of the fact that the input data (managed by GFS [8]) is stored on the local disks of the machines that make up our cluster GFS divides each file into 64 MB blocks, and stores several copies of each block (typically 3 copies) on different machines The MapReduce master takes the location information of the input files into account and attempts to schedule a map task on a machine that contains a replica of the corre-sponding input data Failing that, it attempts to schedule

a map task near a replica of that task’s input data (e.g., on

a worker machine that is on the same network switch as the machine containing the data) When running large MapReduce operations on a significant fraction of the workers in a cluster, most input data is read locally and consumes no network bandwidth

3.5 Task Granularity

We subdivide the map phase intoM pieces and the re-duce phase intoR pieces, as described above Ideally, M andR should be much larger than the number of worker machines Having each worker perform many different tasks improves dynamic load balancing, and also speeds

up recovery when a worker fails: the many map tasks

it has completed can be spread out across all the other worker machines

There are practical bounds on how largeM and R can

be in our implementation, since the master must make O(M + R) scheduling decisions and keeps O(M ∗ R) state in memory as described above (The constant fac-tors for memory usage are small however: theO(M ∗ R) piece of the state consists of approximately one byte of data per map task/reduce task pair.)

Furthermore,R is often constrained by users because

the output of each reduce task ends up in a separate

out-put file In practice, we tend to chooseM so that each

individual task is roughly 16 MB to 64 MB of input data

(so that the locality optimization described above is most

effective), and we makeR a small multiple of the

num-ber of worker machines we expect to use We often

per-form MapReduce computations withM = 200, 000 and

R = 5, 000, using 2,000 worker machines

3.6 Backup Tasks

One of the common causes that lengthens the total time

taken for a MapReduce operation is a “straggler”: a

ma-chine that takes an unusually long time to complete one

of the last few map or reduce tasks in the computation

Stragglers can arise for a whole host of reasons For

ex-ample, a machine with a bad disk may experience

fre-quent correctable errors that slow its read performance

from 30 MB/s to 1 MB/s The cluster scheduling

sys-tem may have scheduled other tasks on the machine,

causing it to execute the MapReduce code more slowly

due to competition for CPU, memory, local disk, or

net-work bandwidth A recent problem we experienced was

a bug in machine initialization code that caused

proces-sor caches to be disabled: computations on affected

ma-chines slowed down by over a factor of one hundred

We have a general mechanism to alleviate the

prob-lem of stragglers When a MapReduce operation is close

to completion, the master schedules backup executions

of the remaining in-progress tasks The task is marked

as completed whenever either the primary or the backup

execution completes We have tuned this mechanism so

that it typically increases the computational resources

used by the operation by no more than a few percent

We have found that this significantly reduces the time

to complete large MapReduce operations As an

exam-ple, the sort program described in Section 5.3 takes 44%

longer to complete when the backup task mechanism is

disabled

4 Refinements

Although the basic functionality provided by simply

writing Map and Reduce functions is sufficient for most

needs, we have found a few extensions useful These are

described in this section

4.1 Partitioning Function

The users of MapReduce specify the number of reduce

tasks/output files that they desire (R) Data gets

parti-tioned across these tasks using a partitioning function on

the intermediate key A default partitioning function is provided that uses hashing (e.g “hash(key) mod R”).

This tends to result in fairly well-balanced partitions In some cases, however, it is useful to partition data by some other function of the key For example, sometimes the output keys are URLs, and we want all entries for a single host to end up in the same output file To support situations like this, the user of the MapReduce library can provide a special partitioning function For example, using “hash(Hostname(urlkey)) mod R” as the

par-titioning function causes all URLs from the same host to end up in the same output file

4.2 Ordering Guarantees

We guarantee that within a given partition, the interme-diate key/value pairs are processed in increasing key or-der This ordering guarantee makes it easy to generate

a sorted output file per partition, which is useful when the output file format needs to support efficient random access lookups by key, or users of the output find it con-venient to have the data sorted

4.3 Combiner Function

In some cases, there is significant repetition in the inter-mediate keys produced by each map task, and the

user-specified Reduce function is commutative and

associa-tive A good example of this is the word counting exam-ple in Section 2.1 Since word frequencies tend to follow

a Zipf distribution, each map task will produce hundreds

or thousands of records of the form <the, 1> All of these counts will be sent over the network to a single

re-duce task and then added together by the Rere-duce function

to produce one number We allow the user to specify an

optional Combiner function that does partial merging of

this data before it is sent over the network

The Combiner function is executed on each machine

that performs a map task Typically the same code is used

to implement both the combiner and the reduce func-tions The only difference between a reduce function and

a combiner function is how the MapReduce library han-dles the output of the function The output of a reduce function is written to the final output file The output of

a combiner function is written to an intermediate file that will be sent to a reduce task

Partial combining significantly speeds up certain classes of MapReduce operations Appendix A contains

an example that uses a combiner

4.4 Input and Output Types

The MapReduce library provides support for reading in-put data in several different formats For example, “text”

is the offset in the file and the value is the contents of

the line Another common supported format stores a

sequence of key/value pairs sorted by key Each input

type implementation knows how to split itself into

mean-ingful ranges for processing as separate map tasks (e.g

text mode’s range splitting ensures that range splits

oc-cur only at line boundaries) Users can add support for a

new input type by providing an implementation of a

sim-ple reader interface, though most users just use one of a

small number of predefined input types

A reader does not necessarily need to provide data

read from a file For example, it is easy to define a reader

that reads records from a database, or from data

struc-tures mapped in memory

In a similar fashion, we support a set of output types

for producing data in different formats and it is easy for

user code to add support for new output types

4.5 Side-effects

In some cases, users of MapReduce have found it

con-venient to produce auxiliary files as additional outputs

from their map and/or reduce operators We rely on the

application writer to make such side-effects atomic and

idempotent Typically the application writes to a

tempo-rary file and atomically renames this file once it has been

fully generated

We do not provide support for atomic two-phase

com-mits of multiple output files produced by a single task

Therefore, tasks that produce multiple output files with

cross-file consistency requirements should be

determin-istic This restriction has never been an issue in practice

4.6 Skipping Bad Records

Sometimes there are bugs in user code that cause the Map

or Reduce functions to crash deterministically on certain

records Such bugs prevent a MapReduce operation from

completing The usual course of action is to fix the bug,

but sometimes this is not feasible; perhaps the bug is in

a third-party library for which source code is

unavail-able Also, sometimes it is acceptable to ignore a few

records, for example when doing statistical analysis on

a large data set We provide an optional mode of

execu-tion where the MapReduce library detects which records

cause deterministic crashes and skips these records in

or-der to make forward progress

Each worker process installs a signal handler that

catches segmentation violations and bus errors Before

invoking a user Map or Reduce operation, the

MapRe-duce library stores the sequence number of the argument

in a global variable If the user code generates a signal,

contains the sequence number to the MapReduce mas-ter When the master has seen more than one failure on

a particular record, it indicates that the record should be skipped when it issues the next re-execution of the corre-sponding Map or Reduce task

4.7 Local Execution

Debugging problems in Map or Reduce functions can be

tricky, since the actual computation happens in a dis-tributed system, often on several thousand machines, with work assignment decisions made dynamically by the master To help facilitate debugging, profiling, and small-scale testing, we have developed an alternative im-plementation of the MapReduce library that sequentially executes all of the work for a MapReduce operation on the local machine Controls are provided to the user so that the computation can be limited to particular map tasks Users invoke their program with a special flag and can then easily use any debugging or testing tools they find useful (e.g gdb)

4.8 Status Information

The master runs an internal HTTP server and exports

a set of status pages for human consumption The sta-tus pages show the progress of the computation, such as how many tasks have been completed, how many are in progress, bytes of input, bytes of intermediate data, bytes

of output, processing rates, etc The pages also contain links to the standard error and standard output files gen-erated by each task The user can use this data to pre-dict how long the computation will take, and whether or not more resources should be added to the computation These pages can also be used to figure out when the com-putation is much slower than expected

In addition, the top-level status page shows which workers have failed, and which map and reduce tasks they were processing when they failed This informa-tion is useful when attempting to diagnose bugs in the user code

The MapReduce library provides a counter facility to count occurrences of various events For example, user code may want to count total number of words processed

or the number of German documents indexed, etc

To use this facility, user code creates a named counter object and then increments the counter appropriately in

the Map and/or Reduce function For example:

uppercase = GetCounter("uppercase");

map(String name, String contents):

for each word w in contents:

if (IsCapitalized(w)):

uppercase->Increment();

EmitIntermediate(w, "1");

The counter values from individual worker machines

are periodically propagated to the master (piggybacked

on the ping response) The master aggregates the counter

values from successful map and reduce tasks and returns

them to the user code when the MapReduce operation

is completed The current counter values are also

dis-played on the master status page so that a human can

watch the progress of the live computation When

aggre-gating counter values, the master eliminates the effects of

duplicate executions of the same map or reduce task to

avoid double counting (Duplicate executions can arise

from our use of backup tasks and from re-execution of

tasks due to failures.)

Some counter values are automatically maintained

by the MapReduce library, such as the number of

in-put key/value pairs processed and the number of outin-put

key/value pairs produced

Users have found the counter facility useful for

san-ity checking the behavior of MapReduce operations For

example, in some MapReduce operations, the user code

may want to ensure that the number of output pairs

produced exactly equals the number of input pairs

cessed, or that the fraction of German documents

pro-cessed is within some tolerable fraction of the total

num-ber of documents processed

In this section we measure the performance of

MapRe-duce on two computations running on a large cluster of

machines One computation searches through

approxi-mately one terabyte of data looking for a particular

pat-tern The other computation sorts approximately one

ter-abyte of data

These two programs are representative of a large

sub-set of the real programs written by users of MapReduce –

one class of programs shuffles data from one

representa-tion to another, and another class extracts a small amount

of interesting data from a large data set

5.1 Cluster Configuration

All of the programs were executed on a cluster that

consisted of approximately 1800 machines Each

ma-chine had two 2GHz Intel Xeon processors with

Hyper-Threading enabled, 4GB of memory, two 160GB IDE

Seconds

0 10000 20000 30000

Figure 2: Data transfer rate over time

disks, and a gigabit Ethernet link The machines were arranged in a two-level tree-shaped switched network with approximately 100-200 Gbps of aggregate band-width available at the root All of the machines were

in the same hosting facility and therefore the round-trip time between any pair of machines was less than a mil-lisecond

Out of the 4GB of memory, approximately 1-1.5GB was reserved by other tasks running on the cluster The programs were executed on a weekend afternoon, when the CPUs, disks, and network were mostly idle

The grep program scans through1010100-byte records, searching for a relatively rare three-character pattern (the pattern occurs in 92,337 records) The input is split into approximately 64MB pieces (M = 15000), and the en-tire output is placed in one file (R = 1)

Figure 2 shows the progress of the computation over time The Y-axis shows the rate at which the input data is scanned The rate gradually picks up as more machines are assigned to this MapReduce computation, and peaks

at over 30 GB/s when 1764 workers have been assigned

As the map tasks finish, the rate starts dropping and hits zero about 80 seconds into the computation The entire computation takes approximately 150 seconds from start

to finish This includes about a minute of startup over-head The overhead is due to the propagation of the pro-gram to all worker machines, and delays interacting with GFS to open the set of 1000 input files and to get the information needed for the locality optimization

5.3 Sort

The sort program sorts1010100-byte records (approxi-mately 1 terabyte of data) This program is modeled after the TeraSort benchmark [10]

The sorting program consists of less than 50 lines of

user code A three-line Map function extracts a 10-byte

sorting key from a text line and emits the key and the

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Done

(c) 200 tasks killed

Figure 3: Data transfer rates over time for different executions of the sort program

original text line as the intermediate key/value pair We

used a built-in Identity function as the Reduce operator.

This functions passes the intermediate key/value pair

un-changed as the output key/value pair The final sorted

output is written to a set of 2-way replicated GFS files

(i.e., 2 terabytes are written as the output of the program)

As before, the input data is split into 64MB pieces

(M = 15000) We partition the sorted output into 4000

files (R = 4000) The partitioning function uses the

ini-tial bytes of the key to segregate it into one ofR pieces

Our partitioning function for this benchmark has

built-in knowledge of the distribution of keys In a general

sorting program, we would add a pre-pass MapReduce

operation that would collect a sample of the keys and

use the distribution of the sampled keys to compute

split-points for the final sorting pass

Figure 3 (a) shows the progress of a normal execution

of the sort program The top-left graph shows the rate

at which input is read The rate peaks at about 13 GB/s

and dies off fairly quickly since all map tasks finish

be-fore 200 seconds have elapsed Note that the input rate

is less than for grep This is because the sort map tasks

spend about half their time and I/O bandwidth writing

in-termediate output to their local disks The corresponding

intermediate output for grep had negligible size

The middle-left graph shows the rate at which data

is sent over the network from the map tasks to the

re-duce tasks This shuffling starts as soon as the first

map task completes The first hump in the graph is for

the first batch of approximately 1700 reduce tasks (the entire MapReduce was assigned about 1700 machines, and each machine executes at most one reduce task at a time) Roughly 300 seconds into the computation, some

of these first batch of reduce tasks finish and we start shuffling data for the remaining reduce tasks All of the shuffling is done about 600 seconds into the computation The bottom-left graph shows the rate at which sorted data is written to the final output files by the reduce tasks There is a delay between the end of the first shuffling pe-riod and the start of the writing pepe-riod because the ma-chines are busy sorting the intermediate data The writes continue at a rate of about 2-4 GB/s for a while All of the writes finish about 850 seconds into the computation Including startup overhead, the entire computation takes

891 seconds This is similar to the current best reported result of 1057 seconds for the TeraSort benchmark [18]

A few things to note: the input rate is higher than the shuffle rate and the output rate because of our locality optimization – most data is read from a local disk and bypasses our relatively bandwidth constrained network The shuffle rate is higher than the output rate because the output phase writes two copies of the sorted data (we make two replicas of the output for reliability and avail-ability reasons) We write two replicas because that is the mechanism for reliability and availability provided

by our underlying file system Network bandwidth re-quirements for writing data would be reduced if the un-derlying file system used erasure coding [14] rather than replication

5.4 Effect of Backup Tasks

In Figure 3 (b), we show an execution of the sort

pro-gram with backup tasks disabled The execution flow is

similar to that shown in Figure 3 (a), except that there is

a very long tail where hardly any write activity occurs

After 960 seconds, all except 5 of the reduce tasks are

completed However these last few stragglers don’t

fin-ish until 300 seconds later The entire computation takes

1283 seconds, an increase of 44% in elapsed time

5.5 Machine Failures

In Figure 3 (c), we show an execution of the sort program

where we intentionally killed 200 out of 1746 worker

processes several minutes into the computation The

underlying cluster scheduler immediately restarted new

worker processes on these machines (since only the

pro-cesses were killed, the machines were still functioning

properly)

The worker deaths show up as a negative input rate

since some previously completed map work disappears

(since the corresponding map workers were killed) and

needs to be redone The re-execution of this map work

happens relatively quickly The entire computation

fin-ishes in 933 seconds including startup overhead (just an

increase of 5% over the normal execution time)

We wrote the first version of the MapReduce library in

February of 2003, and made significant enhancements to

it in August of 2003, including the locality optimization,

dynamic load balancing of task execution across worker

machines, etc Since that time, we have been pleasantly

surprised at how broadly applicable the MapReduce

li-brary has been for the kinds of problems we work on

It has been used across a wide range of domains within

Google, including:

• large-scale machine learning problems,

• clustering problems for the Google News and

Froogle products,

• extraction of data used to produce reports of popular

queries (e.g Google Zeitgeist),

• extraction of properties of web pages for new

exper-iments and products (e.g extraction of

geographi-cal locations from a large corpus of web pages for

localized search), and

• large-scale graph computations

2003/03 2003/06 2003/09 2003/12 2004/03 2004/06 2004/09

0 200 400 600 800

Figure 4: MapReduce instances over time

Average job completion time 634 secs Machine days used 79,186 days

Intermediate data produced 758 TB Output data written 193 TB Average worker machines per job 157 Average worker deaths per job 1.2 Average map tasks per job 3,351 Average reduce tasks per job 55

Unique map implementations 395

Unique reduce implementations 269

Unique map/reduce combinations 426

Table 1: MapReduce jobs run in August 2004

Figure 4 shows the significant growth in the number of separate MapReduce programs checked into our primary source code management system over time, from 0 in early 2003 to almost 900 separate instances as of late September 2004 MapReduce has been so successful be-cause it makes it possible to write a simple program and run it efficiently on a thousand machines in the course

of half an hour, greatly speeding up the development and prototyping cycle Furthermore, it allows programmers who have no experience with distributed and/or parallel systems to exploit large amounts of resources easily

At the end of each job, the MapReduce library logs statistics about the computational resources used by the job In Table 1, we show some statistics for a subset of MapReduce jobs run at Google in August 2004

6.1 Large-Scale Indexing

One of our most significant uses of MapReduce to date has been a complete rewrite of the production